Map generation device, map delivery method, and map generation program

ABSTRACT

To provide a map generation device according to the present invention which extracts a polygon shape of a building having a complex upper portion structure from a wide area image. The map generation device includes an image appointment unit that receives appointment of at least one position in a building existing within an aerial photograph, a polygon extraction unit that extracts a building region based on a result of discriminating a color around the appointed position and extracts a polygon line of the building region, and a vector generation unit that generates a vector of the polygon line of the building region.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention relates to a map generation device thatgenerates a map by extracting a polygon shape of a building roof from anaerial photograph (a photograph taken from a satellite or an airplane).

[0003] 2. Description of the Related Art

[0004] Up to now, examples of a method of generating a map from anaerial photograph include a method of using an analytical plotter tomanually trace a polygon shape of a building shown in an image, and amethod of automatically extracting a building polygon shape from animage.

[0005] In the method of using an analytical plotter, two imagesphotographed with parallax are estimated and aligned to generate astereo image. Then, an operator extracts a building polygon shape fromthe stereo image by a handle operation (see, for example, UjihisaKimoto, “Actuality of photogrammetry”, Sankaido, P.91-97).

[0006] Meanwhile, in the method of automatically extracting a buildingpolygon shape, a monocular image is used along with informationincluding a position, an angle, and a sun direction where the image wasphotographed to extract regions of a rectangular roof, a wall, a shadow,and the like based on linearity and positional relationships involved ininformation on lines extracted as edge images. Thus, existence of abuilding can be discriminated. For example, there is proposed a methodof extracting a rectangular roof shape by setting information on theposition, the angle, and the sun direction where the image wasphotographed as known conditions, and tentatively setting a buildingstructure based on the line information obtained from the edge images(see, for example, C. O. Jaynes, two others, “Task Driven PerceptualOrganization for Extraction of Rooftop Polygons”, Proceedings of ARPAImage Understanding workshop, 1994, P.359-365). There is also proposed amethod of extracting stereo information on a rectangular building bysetting information on the sun direction and a camera orientation wherethe image was photographed as known conditions, and discriminating localcharacteristics, relative positional relationships, and the likeinvolved in the line information of a roof, a wall, and a shadow (see,for example, C. Lin, one other, “Building Detection and Description froma Single Intensity Image”, Computer Vision and Image Understanding,Vol.72, No.2, November 1989, P.101-121).

[0007] According to the above-mentioned conventional method ofgenerating a map using an analytical plotter, extraction of a buildingpolygon shape is conducted by the operation of an operator, making itdifficult to correctly trace the building polygon shape. Further, theabove-mentioned conventional method of automatically extracting abuilding region can be applied only to a building whose roof shape has asimple structure such as a flat roof shape. The building region cannotbe extracted when sunlight falls on a roof such as a gable roof atdifferent angles, producing gray contrast, or when a building has astructure such as an exhaust tower on its roof.

SUMMARY OF THE INVENTION

[0008] It is an object of the present invention to (1) extract abuilding polygon shape from a wide area image, (2) generate a mapefficiently by reducing operations conducted by an operator, and (3)extract a polygon shape of a building having a complex upper portionstructure such as a gable roof or an exhaust tower.

[0009] According to the present invention, there is provided a mapgeneration device including an image appointment unit that receivesappointment of at least one position in a building existing within anaerial photograph, a polygon extraction unit that extracts a buildingregion based on a result of discriminating a color around the appointedposition and extracts a polygon line of the building region, and avector generation unit that generates a vector of the polygon line ofthe building region.

[0010] According to the present invention, the building polygon shape isextracted in a position where a building region is appointed in advance,enabling the extraction of a wide area building polygon shape. Inaddition, by appointing a building roof, a processing range forestimation of the building roof can be limited, enabling the extractionof a polygon shape of the entire building.

[0011] Accordingly, map generation becomes easy, and the map generationcan be realized in a short period of time and at low cost.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012]FIG. 1 is a diagram for explaining polygon shape extraction andmap generation based on an appointed position.

[0013]FIG. 2 is a functional block diagram of a map generation deviceaccording to an embodiment of the present invention.

[0014]FIG. 3 is a diagram for explaining feature classification ofbuildings.

[0015]FIG. 4 is a flowchart of a building polygon shape extractionprocess according to the embodiment of the present invention.

[0016]FIG. 5 is a flowchart of a building roof texture analysis processaccording to the embodiment of the present invention.

[0017]FIG. 6 is a diagram for explaining selection of sample pixels formatching according to the embodiment of the present invention.

[0018]FIG. 7 is a flowchart of a polygon shape extraction processaccording to the embodiment of the present invention.

[0019]FIG. 8 is a diagram for explaining extraction of a building regionaccording to the embodiment of the present invention.

[0020]FIG. 9 is a diagram for explaining range correction of a buildingregion according to the embodiment of the present invention.

[0021]FIG. 10 is a diagram for explaining a building region smoothingprocess according to the embodiment of the present invention.

[0022]FIG. 11 is a flowchart of a polygon shape correction processaccording to the embodiment of the present invention.

[0023]FIG. 12 is a diagram for explaining correction of polygon linesaccording to the embodiment of the present invention.

[0024]FIG. 13 is a diagram for explaining position correction of polygonlines according to the embodiment of the present invention.

[0025]FIG. 14 is a flowchart of a building region integration processaccording to the embodiment of the present invention.

[0026]FIG. 15 is a diagram for explaining integration patterns for abuilding region according to the embodiment of the present invention.

[0027]FIG. 16 is a diagram for explaining other integration patterns fora building region according to the embodiment of the present invention.

[0028]FIG. 17 is a flowchart of a process for ground projection of apolygon shape according to the embodiment of the present invention.

[0029]FIG. 18 is a diagram for explaining the ground projection of apolygon shape according to the embodiment of the present invention.

[0030]FIG. 19 is a diagram for explaining transmission of mapinformation utilizing a map generation device according to theembodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0031] A map generation device as described in the present invention isaimed at map generation using a monocular image as well as an orthoimage and a stereo image. Also, the map generation device generates amap from a monochrome image as well as from a color image. Instead ofconducting manual plotting or fully automatic recognition, as shown inFIG. 1, the map generation device appoints a building roof on an imageto limit a processing range for estimation of the building roof,extracts a polygon shape of a building region that includes an appointedposition, and projects the polygon shape of the building on a ground,thus obtaining a figure in a map.

[0032]FIG. 2 is a functional block diagram of a map generation deviceaccording to an embodiment of the present invention.

[0033] The map generation device according to the present inventionincludes a control unit 201, an image appointment unit 202, a rooftexture analysis unit 203, a polygon extraction unit 204, a polygoncorrection unit 205, a temporary memo unit 206, a temporary database207, a structural analysis and integration unit 208, a ground projectionunit 209, a vector generation unit 210, a storage unit for buildingpolygon data 211, a vector map database 212, and a reception unit forcorrection 213.

[0034] The control unit 201 supervises the map generation deviceaccording to this embodiment, and controls an operation of each unit ina building polygon shape extraction process. In other words, the controlunit 201 determines the next function to be activated in a buildingpolygon shape extraction process. More specifically, the control unit201 receives instructions relating to image input, start and end ofbuilding polygon shape extraction, result deletion, result accumulation,and building-structural integration, monitors an execution state of thebuilding polygon shape extraction process, and determines the functionto be activated next.

[0035] The image appointment unit 202 receives appointment of at leastone position on a building of an aerial photograph whose polygon shapeis to be extracted.

[0036] The roof texture analysis unit 203 analyzes features such ascolors and textures around the position whose input instruction isreceived by the image appointment unit 202 to thereby determine samplecolors for matching, and a discrimination threshold and a searchingrange for building region detection.

[0037] The polygon extraction unit 204 discriminates a similarity (forexample, a similarity in gray level) to the sample colors for matchingdetermined by the roof texture analysis unit 203 within the searchingrange for region detection also determined by the roof texture analysisunit 203, and extracts building region pixels having a similar color tothe sample colors for matching. Then, the polygon extraction unit 204uses information on a boundary around the extracted building region tocorrect the building region range, and uses the boundary of the buildingregion to extract polygon lines according to an inclination angle of thebuilding region.

[0038] The polygon correction unit 205 uses a linking pattern to correctthe polygon lines extracted by the polygon extraction unit 204, and usesboundary information of a plurality of building regions to correct thepositions of the polygon lines.

[0039] The temporary memo unit 206 temporarily stores intermediateresult data for the building polygon shape corrected by the polygoncorrection unit 205.

[0040] The temporary database 207 accumulates the intermediate resultdata for the building polygon shape.

[0041] In the case where the building roof has distinct colors ordistinct structures such as a building with a gable roof, the structuralanalysis and integration unit 208 estimates the building region based onbuilding structural knowledge, and integrates the building region. Inthe case where the entire polygon line cannot be extracted by just oneappointment, a plurality of positions are appointed to integrate theappointed building regions.

[0042] In order to convert the extracted building polygon shape into adiagram on a ground, the ground projection unit 209 uses a ridge line ofa vertical direction of the building to project the polygon line of thebuilding region on the ground.

[0043] The vector generation unit 210 generates the building polygonshape based on vectors from the polygon line of the building regionobtained by the ground projection unit 209.

[0044] The storage unit for building polygon data 211 stores vectorinformation of the polygon lines of the building region generatedconclusively.

[0045] The vector map database 212 accumulates vector map informationobtained from the image.

[0046] The reception unit for correction 213 receives correction of thegenerated vector map information conducted by an operator.

[0047]FIG. 3 is a diagram for explaining feature classification ofbuildings.

[0048] The buildings can be classified into a building 301 whose upperstructure (roof) has a single color (single gray level) and buildings302 to 305 whose roofs each have a combination of a plurality of colors(combination of multiple colors). The buildings having the combinationof a plurality of colors can be classified into the building 302 whoseroof has a partial color variance (for example, a portion exhibiting acolor variance due to exposure to wind and rain) and the buildings 303to 305 each having multiple colors due to multiple structures of theroof. The buildings having the multiple structures can be classifiedinto the building 303 having portions with different gray levels due todifferent lighting angles of sunlight because its roof is not flat, thebuilding 304 provided with a small-scale structure such as an exhausttower or a bay window, and the building 305 whose roof is constructed oflarge-scale multiple structures.

[0049] The features of those building roofs can be discriminated basedon gray levels in the case of using a monochrome photograph, but need tobe discriminated based on the colors of the roof in the case of using acolor photograph. In the latter case, the features are discriminated byobtaining a gray level for each of the three primary colors (red, green,and blue). That is, the colors are discriminated based on the graylevels. Also, the sample colors for matching which will be describedlater are defined by the respective gray levels of the three primarycolors.

[0050]FIG. 4 is a flowchart of the building polygon shape extractionprocess according to the embodiment of the present invention.

[0051] First, an operator appoints a position on a building whosepolygon shape is to be extracted and which is shown in an aerialphotograph (step 401). It should be noted that the appointment of theposition on the building may be conducted for one point on the buildingor a region having a certain area. Then, the gray levels or the texturesof a roof of the appointed building are analyzed, and sample pixels formatching are extracted based on analysis results for gray levels of thebuilding roof, and the sample colors for matching, the discriminationthreshold for matching, and a searching range for region detection aredetermined (step 402).

[0052] It is then determined whether the pixels within the searchingrange for region detection are within the range of the threshold withrespect to the sample colors for matching set in step 402, and thepolygon shape of the building roof is extracted (step 403).

[0053] Next, a polygon shape correction process is conducted (step 404).In the polygon shape correction process, the polygon lines extracted instep 403 are matched with a predetermined linking pattern to correct thebuilding polygon shape. Further, the boundary of the building region isused to correct the positions of the polygon lines.

[0054] Next, a structural analysis and integration process is conducted(step 405). In the structural analysis and integration process, thestructure of the building roof is analyzed, and the spreading of theroof is recognized. Here, the building roof is estimated based on roofstructural knowledge, the building region is also estimated, and thesepieces of information are integrated. In the case where the entirebuilding region cannot be extracted by just one appointment, a pluralityof positions are appointed to extract a plurality of building regionsand integrate the extracted building regions.

[0055] Next, a process for ground projection of a polygon shape isconducted (step 406). The polygon line of the building region which isextracted by the processes of up to step 405 is not limited to a line ata ground level. A high-rise building in the periphery of the aerialphotograph has been photographed from an inclined direction, making itnecessary to convert the building roof into a ground-level diagram.Therefore, the ridge line of the vertical direction of the building isused to project the polygon lines of the building region on the ground.

[0056] Subsequently, description will be made of the details of thebuilding polygon shape extraction process shown in FIG. 4.

[0057]FIG. 5 is a flowchart of a building roof texture analysis process(step 402 of FIG. 4).

[0058] Specifically, a gray-level variance of pixels is first computedaround the appointed position (step 501). In the gray-level variancecomputation process, pixels included in a predetermined region (forexample, 10 dots×10 dots) with the position appointed in step 401 beingset at its center are extracted, and the gray-level variance of theextracted pixels is computed.

[0059] Next, the sample pixels for matching are selected (step 502). Inthe process for selection of the sample pixels for matching, based on acomparison result between the gray-level variance of the pixels aroundthe appointed position obtained in step 401 and the predeterminedthreshold, the selection is made of the pixels for determining thesample colors for matching for building region extraction. That is, ifthe gray-level variance is larger than the predetermined threshold, onlythe appointed position is selected for the sample pixel for matching.This is because variation in gray level is large, so that thediscriminating threshold becomes too large by the below-described methodof determining the discriminating threshold-based on the variance,making it difficult to accurately extract the building region.

[0060] On the other hand, if the gray-level variance is equal to orsmaller than the predetermined threshold, a predetermined number of thesample pixels for matching are selected from the predetermined regionwith the appointed position being set at its center. The predeterminedregion may be set to a range of approximately 15 dots×15 dots because acertain area is necessary for the range to be used for matching. FIG. 6shows the selection of the sample pixels for matching. The appointedposition is set as the center of the predetermined range, and positions(for example, four vertices for the predetermined region being aquadrangle) that are most apart from the appointed position in diagonaldirections of the predetermined region are selected as the sample pixelsfor matching (represented by  in the drawing).

[0061] In addition, positions that are apart from the already-determinedfive sample pixel positions for matching (including the appointedposition) are selected as the sample pixels for matching. For example,positions that are located between the positions on lines passing theappointed position lengthwise and crosswise are selected as the samplepixels for matching (represented by O in the drawing). Accordingly, ninesample pixels for matching including the appointed position can beselected.

[0062] Next, the sample colors (gray levels) for matching are computed(step 503). In the process for computation of the sample colors formatching, an average is computed for the colors (gray levels) of thepixels included in a predetermined region with each of the sample pixelsfor matching selected in step 502 being set at its center. Informationon the predetermined region may be obtained from a neighboring region(for example, 3 dots×3 dots) with the sample pixel for matching beingset at its center in order to compute a gray-level average and reducenoise.

[0063] Next, the discrimination threshold and the searching range forbuilding region detection are selected (step 504). In the process forselection of the discrimination threshold and the searching range forbuilding region detection, the discrimination threshold for buildingregion detection is first determined based on the color (gray-level)variance obtained in step 501. More specifically, by referencing amatching table that stores discrimination thresholds corresponding tothe gray-level variances divided into several ranges, the discriminationthreshold for building region detection is determined based on thegray-level variance. The discrimination thresholds stored in thematching table are set such that when the gray-level variance is large(when the colors are dispersed), a wide-range region is included in thebuilding region. It should be noted that the discrimination thresholdmay be operated using the gray-level variance as a predeterminedfunction (for example, a linear function for multiplying the gray-levelvariance by a predetermined value) to determine the discriminationthreshold for building region detection.

[0064] The searching range for building region detection is determinedbased on the color (gray-level) variance obtained in step 501. Thesearching range is set such that when the gray-level variance is small,peripheral pixels in a wide range are searched through, and when thegray-level variance is large, peripheral pixels in a narrow range aresearched through. More specifically, by referencing a matching tablethat stores searching ranges corresponding to steps in gray-levelvariance, the searching range for building region detection isdetermined based on the gray-level variance. It should be noted that thesearching range for building region detection may be operated using thegray-level variance as the predetermined function (for example, thelinear function for multiplying the gray-level variance by thepredetermined value) to determine the searching range for buildingregion detection.

[0065] Thus, In the building roof texture analysis process (FIG. 5), thefeatures of the colors or the textures of an image within apredetermined range adjacent to the position appointed in step 401 areanalyzed (step 402), and based on analysis results for building rooftextures, the sample colors for matching, the discrimination thresholdfor matching, and the searching range for region detection aredetermined as parameters for region extraction. Accordingly, even in thecase where the building roof has a uniform color or has different colorsdepending on positions thereon, the appropriate parameters for regionextraction can be determined.

[0066]FIG. 7 is a flowchart of a polygon shape extraction process (step403 of FIG. 4).

[0067] The steps for the extraction of the polygon lines of the buildingregion are divided into two groups consisting of the building regionextraction (steps 701 to 706) and region polygon line extraction (steps707 and 708).

[0068] In the building region extraction process, the sample colors formatching (gray levels) obtained in step 503 and the parameters forregion extraction (the discrimination threshold for building regiondetection and the searching range for building region detection)obtained in step 504 are used to extract the building region. In thiscase, the building region detection is conducted in reference to thepixels around the appointed position by following four rules describedbelow.

[0069] (1) The pixels to be included in the building region areextracted from the searching range for region detection around thebuilding region that has been already extracted.

[0070] (2) The color (gray level) of the pixel is compared with theneighboring pixels within the building region that has been alreadyextracted as well as with the sample pixels for matching, and a changeamount of the color (gray level) is determined, thereby discriminatingwhether the searched pixel belongs to the building region or not.

[0071] (3) In the case where the colors (gray levels) of the pixels inthe building region that has been already extracted and those of thesearching range exhibit a sudden change, it is determined that theboundary of the building has been reached, and the detection stops.Also, a region beyond the extracted building boundary is not extractedas the building region.

[0072] (4) The building region is corrected using the information on thebuilding region that has been already extracted and lines around thebuilding region.

[0073] Further, in the polygon line extraction process according to thisembodiment, the vertices of the polygon of the building region areextracted, and the polygon is traced to extract a closed polygon shape.

[0074] More specifically, the edge image is first computed (step 701).In the edge image computation process, a direction and changes in color(gray level) with respect to the adjacent pixels are computed for everypixel. Then, a direction indicating a great gray gradient for therespective pixels is obtained from the direction indicating the largestchange in color. The direction of the gray gradient is represented, forexample, within a range of 0° to 180° by setting a north-south directionas a reference axis (the north-south direction as 0°, and an east-westdirection as 90°). Also, the color (gray level) is converted into an8-bit digital form, and a change value is represented by 0 to 255 in 256steps. The pixels having a larger change value in color than thepredetermined threshold are detected to be utilized as edge pixels.

[0075] Next, the pixels with a similar color (gray level) are extracted(step 702). In the process for extraction of the pixels with a similarcolor, the pixels having a color (gray level) similar to the samplecolor (gray level) for matching obtained in step 503 are extracted.Here, in the case where the number of the pixels of the edge image thatis included in the predetermined region around the pixels of thebuilding region already extracted is smaller than the predeterminedthreshold, the pixels with a similar color (gray level) are set ascandidates for the building region. In the other case where the numberof the pixels of the edge image that is included in the predeterminedregion around the pixels of the building region already extracted islarger than the predetermined threshold, the pixels with a similar color(gray level) are not set as the candidates for the building regionbecause the extracted pixels are discriminated as being close to theboundary. Then, the sample pixels for matching selected in the formercase are set as representative pixels for the building region.

[0076] In the case where only a pixel of the appointed position isselected for the sample pixel for matching in step 502 (in the casewhere only a single sample pixel for matching is selected), a differencebetween the color (gray level) of the sample pixel for matching and thecolors (gray levels) of the candidate pixels is computed using aEuclidean distance. Then, based on the comparison result between agray-level difference and the discrimination threshold for buildingregion detection, it is determined whether the candidate pixels are setas the pixels for the building region or not. More specifically, whenthe gray-level difference (Euclidean distance) is smaller than thediscrimination threshold for building region detection, the candidatepixel is set as the pixels for the building region.

[0077] Meanwhile, in the case where nine sample pixels for matching areselected, as shown in Expression 1, the gray-level differences among thenine sample pixels for matching are computed using the Euclideandistance. If two or more pixels of the nine sample pixels for matchinghave smaller gray-level differences (Euclidean distances) with respectto the colors (gray levels) of the candidate pixels than thediscrimination threshold for building region detection (CM(i,j)≧2), thecandidate pixels are extracted as building region pixels.

[0078] In Expression 1, CM(i,j) represents the number of the samplepixels for matching having a smaller degree of color matching for apixel (i,j) than the discrimination threshold for building regiondetection.

[0079] In Expression 2, f(x) represents a function for discriminatingwhether a condition x is true or false. When x is true, f(x) returns 1,and when x is false, f(x) returns 0. TH represents a discriminationthreshold for building region detection.

[0080] Expression 3 represents a value of the color (gray level) of thepixel (i,j) (m=R, G, B).

[0081] Expression 4 represents a sample color for matching (gray level)(m=R, G, B).

[0082] Further, comparison matching is conducted between the candidatepixels and the pixels in the searching range for region detection aroundthe candidate pixels as well as between the candidate pixels and thesample pixels for matching. In other words, the differences between thegray levels of the candidate pixels and the gray levels of the pixels inthe searching range for region detection are computed using theEuclidean distances. Then, based on the comparison results between thedistances and the discrimination threshold for building regiondetection, it is determined whether the candidate pixels are set as thepixels for the building region or not. More specifically, when thegray-level difference (Euclidean distance) is smaller than thediscrimination threshold for building region detection, the candidatepixel is set as the pixel for the building region. When the gray-leveldifference is equal to or larger than the discrimination threshold forbuilding region detection, the candidate pixel is not set as the pixelfor the building region because the candidate pixel is discriminated asbeing excluded from the building region.

[0083] As shown in FIG. 8, by repeating the above-mentioned process ofstep 702, the range of the building region is expanded toward thevicinities from the building region (appointed position) that has beenalready extracted, thereby extracting the building region.

[0084] Next, the building region is corrected (step 703). In thebuilding region correction process, the building region is correctedusing the information on the edge image obtained in step 701 in order toenhance an accuracy of extracting the building region. In other words,edge information of the edge image of the building region is used tolink the pixels having the same gray gradient direction with each otherby Hough transformation, and obtain an inclination angle for the graygradient and end points of a line segment. After that, the obtained lineinformation is used to correct the building region. In the correctionprocess, as shown in FIG. 9, lines 602, which neighbor to each other soas to surround a building region 601 extracted in step 702, areselected, and the building region is expanded to the neighboring lines602.

[0085] Next, a region inclination angle is computed (step 704). In theregion inclination angle computation process, the longest boundary amongthe boundaries of the building region corrected in step 703 is selectedto be set for the region inclination angle.

[0086] Next, the building region is rotated (step 705). In the buildingregion rotation process, with respect to the region inclination angleobtained in step 704, linear transformation according to affinetransformation and parallel displacement are conducted to rotate thebuilding region. The building region is often formed of a rectangle or acombination of rectangles, so that the boundary becomes horizontal orvertical with respect to an axis direction. This makes it possible tosimplify an operation for processing the polygon extraction, improve anoperational speed, and enhance an operational accuracy.

[0087] Next, smoothing is conducted on the building region (step 706).In the region smoothing process, as shown in FIG. 10, unevenness of theboundary of the building region is eliminated and corrected into astraight line. Also, a hole within the region is filled in to correctthe building region.

[0088] Next, polygon lines of the building region are extracted (step707). In the polygon line extraction process, vertices on the boundaryof the building region are first detected as polygon feature points.Then, the polygon feature points are connected (traced) to therebyextract the polygon lines as vector information.

[0089] Next, a polygon line is corrected into a straight line (step708). In the polygon line correction process, the polygon line composedof a plurality of short line segments is corrected into a singlestraight line. For example, short line segments that are connected in azigzag manner (stepwise) are transformed into a single oblique line.

[0090] After that, the vector information of the polygon line of thebuilding region is rotated in the reverse direction at the same angle asthat in step 705 to return the building region to a normal direction.

[0091] As described above, according to the building region extractionprocess, the parameters for region extraction obtained in steps 503 and504 are used to extract the building region including the appointedposition. As a result, in the case where the building roof has a uniformcolor or has different colors depending on positions thereon, thebuilding region can be extracted appropriately.

[0092] Further, according to the polygon line extraction process, thevertices of the polygon of the building region are extracted and tracedto have a closed polygon shape. As a result, the vector information ofthe polygon lines of the building region can be obtained.

[0093]FIG. 11 is a flowchart of the polygon shape correction (step 404of FIG. 4).

[0094] In the polygon shape correction process, in order to furtherenhance an accuracy in building polygon shape, based on the fact thatthe building region is often formed of straight lines, the polygon linesof the building region are corrected so as to be aligned with the actualboundary of the building region. As the polygon shape correction, thecorrection of the polygon shape (steps 1101 to 1103) and the positioncorrection of the polygon lines (steps 1104 to 1106) are conducted.

[0095] First, lengths and directions of the polygon lines of thebuilding region are computed (step 1101). In the polygon linecomputation process, the length of each polygon line and an anglebetween each polygon line and the horizontal direction are computed.

[0096] Next, the polygon shape is corrected (step 1102). In the polygonshape correction process, as shown in FIG. 12, the polygon line iscorrected when the polygon line corresponds to a predeterminedcorrection pattern. For example, in the case where a short lineinterposes between long lines, the three lines are judged as being acontinuous straight line, and the short line is eliminated to connectthe two long lines (P1 of FIG. 12). Alternatively, in the case where ashort line exists near the intersection point between two lines, the twolines are judged as crossing, and the short line is eliminated to crossthe two long lines (P2 of FIG. 12). Further, in the case where adjacenttwo straight lines are in a positional relationship of a substantiallyright angle (for example, the two lines cross at an angle of 85° to95°), the two lines are judged as crossing at the right angle, and theintersection point between the two lines are corrected so as to form theright angle between the two long lines (P4 of FIG. 12). Furthermore, inthe case where adjacent two straight lines cross at an angle of 170° to190°, the two lines are judged as being a single line, and the two linesare integrated into a single straight line (P5 of FIG. 12).

[0097] Then, it is discriminated whether a line corresponding to apredetermined pattern remains in the polygon lines of the buildingregion or not, and it is determined whether the whole shape correctionhas finished or not (step 1103). When the shape correction isdiscriminated as being unfinished, the procedure returns to step 1102 tofurther conduct the polygon shape correction process.

[0098] On the other hand, when the whole shape correction isdiscriminated as having finished, the procedure advances to step 1104 tocompute a distribution histogram of the whole boundary lines of thebuilding region. In the process for computation of region boundarydistribution histogram (step 1104), the boundary lines are projected inthe horizontal direction and the vertical direction to compute thedistribution histogram of the boundary lines of the building region. Thehistograms are computed for all the extracted buildings, and acumulative operation is conducted on the histograms for statisticalprocess.

[0099] Next, a boundary position is detected (step 1105). In theboundary position detection process, peak positions of polygon linehistograms are detected from the respective histograms in the horizontaldirection and the vertical direction which are obtained in step 1104.The respective peak positions obtained in the horizontal and verticaldirections are used to form a grid (composed of, for example, grid lines1302 to 1305 shown in FIG. 13) on the image.

[0100] Next, position correction of the polygon lines is conducted (step1106). In the polygon position correction process, the polygon lines inthe horizontal direction and the vertical direction are moved to aposition of the nearest grid, and the polygon lines whose positions havebeen corrected can be obtained. For example, as shown in FIG. 13, thegrid lines 1302 to 1305 formed in the peak positions of the histogramsdetected in the horizontal and vertical directions are used to move theposition of a polygon 1301, and a polygon 1306 whose position has beencorrected can be obtained.

[0101] As described above, according to the polygon shape correctionprocess, the building polygon shape based on the features of thebuilding polygon shape (that is, using the linearity or orthogonality ofthe building polygon lines) is corrected, making it possible to enhancethe accuracy in building polygon shape. Further, according to thepolygon position correction, since the building polygons are liable togather in particular positions such as positions along a road, thebuilding polygon shape is corrected into a correct position, making itpossible to enhance the accuracy in position of the building polygonshape.

[0102]FIG. 14 is a flowchart of analysis and integration of the buildingregion (step 405 of FIG. 4).

[0103] In the building region integration process, as in the case of,for example, the building with a gable roof, a building roof having aninclined surface causes a difference in color (gray level) of the roofsurface depending on how the sunlight falls on the roof, and may beextracted as a different building region, so that a plurality of thebuilding regions need to be integrated. Therefore, the building regionsare integrated based on the boundary of the extracted building regionand lines within the building region.

[0104] First, detection is conducted for the boundary of the buildingregion and the lines within the building region (step 1401). In the lineextraction process, the boundary of the building region extracted instep 703 and information on the lines within the building region areutilized to detect the line.

[0105] Next, the cross-line groups are detected (step 1402). In thecross-line group detection process, lines that cross each other aredetected from among the lines extracted in step 1401, and the lines aregrouped. Then, a crossing position and a crossing angle are computed forthe grouped lines.

[0106] Next, a shape pattern of the cross-lines is discriminated (step1403). In the process for discriminating the shape pattern of thecross-lines, it is discriminated whether the positional relationship(the crossing position and the crossing angle) of the cross-linescorresponds to any predetermined integration patterns shown in FIGS. 15and 16 or not. More specifically, the integration patterns that show anupper portion structure of the building to be integrated include aT-shaped pattern in which one line contacts an end point of another lineand the two lines are in an orthogonal relationship, an X-shaped patternin which two lines cross each other, a Y-shaped pattern in which threelines meet at one point, and V-shaped patterns (having three types, V1,V2, and V3) in which one end of a line coincides with one end of anotherline.

[0107] Next, it is discriminated presence or absence of thecorresponding integration pattern (step 1404). In the process fordiscriminating presence or absence of the corresponding integrationpattern, it is discriminated whether the boundary of the building regionor the lines inside the building region correspond to any integrationpatterns shown in FIGS. 15 and 16 or not. When there exist linescorresponding to any of the integration patterns T, X, Y, V1, V2, andV3, the procedure advances to the process of step 1405. When there existno lines corresponding to any of the integration patterns, the processfor integrating the building structure finishes.

[0108] Next, the building region is estimated (step 1405). In thebuilding region estimation process, when the lines correspond to theintegration pattern T, X, or Y shown in FIG. 15, an axis that passes theintersection point between the lines and has a smallest moment of thelines is obtained. Then, with the axis direction being set as areference, the smallest range of a rectangle containing the lines thatare composed of a crossing pattern is computed. Alternatively, when thelines correspond to the line pattern V1 or V3 shown in FIG. 16, with thedirection of the longest line being set as a reference, the smallestrange of a rectangle containing the lines that are composed of acrossing pattern is computed, and is included in the building region.Further, when the lines correspond to the line pattern V2 shown in FIG.16, with a line orthogonal to a direction of the longest line being acenter axis, it is estimated that the building region exists also in theopposite side, and this is also included in the building region.

[0109] Next, the building region is integrated (step 1406). In thebuilding region integration process, a new building region is generatedby adding the estimated building region to the original building region.After that, the procedure returns to step 1401, and the integrationpattern is detected for the lines of a new building region.

[0110] Then, when the building structure integration process finishes,the procedure returns to step 402, and a polygon shape is extracted froma newly integrated building region (step 403). On the other hand, whenthere remains no building region to be integrated, the procedureadvances to the process for ground projection of a polygon shape (step406).

[0111] As to the building that cannot be integrated even by using theintegration patterns shown in FIGS. 15 and 16, the operator appoints abuilding region composing one building to thereby integrate the buildingregion.

[0112] As described above, according to the building region integrationprocess, from the comparison results between the integration patternpredetermined based on building structural knowledge and the lines ofthe extracted building region, a plurality of upper portion structuresincluded in the same building can be recognized and integrated, and thebuilding having a more complex upper portion structure can be detected.

[0113]FIG. 17 is a flowchart of the process for ground projection of apolygon shape (step 406 of FIG. 4).

[0114] In the process for ground projection of a polygon shape, lines(ridge lines: 1802 to 1804 of FIG. 18) formed of arrises betweenbuilding walls in the vertical direction are first detected (step 1701).In the ridge line detection process, the same method as in step 703 isused with respect to polygon information (1801 of FIG. 18) of theextracted building region to detect the ridge lines (1802 to 1804 ofFIG. 18), whose difference between the length and the inclination angleis smaller than the predetermined threshold, around the vertices of thepolygon shape.

[0115] Next, a distance between a roof and a ground is computed (step1702). In the process for computation of the distance between a roof anda ground, a mean value is computed of the lengths of the detected ridgelines to be set as the distance between the roof and the ground.

[0116] Next, a reduction scale of a roof shape is computed (step 1703).In the process for computation of the reduction scale, ridge lines thatextend from the periphery of two adjacent vertices of the roof shape areselected. Then, the reduction scale is computed of a polygon line of theroof shape located between the two ridge lines when the polygon linemoves in parallel along the two ridge lines from the roof level to theground level, to be set as the reduction scale of the roof shape.

[0117] Next, the polygon shape of the building region is projected onthe ground (step 1704). In the ground projection process, the distancebetween the roof and the ground obtained in step 1702 and the reductionscale obtained in step 1703 are used to conduct linear transformationaccording to the affine transformation and parallel displacement on thepolygon shape of the building region, thereby projecting the reducedpolygon shape of the building region on the ground. As a result, theshape (1805 of FIG. 18) on the ground level can be obtained.

[0118] As described above, according to the process for groundprojection of a polygon shape, the lines (ridge lines) formed of arrisesbetween the building walls in the vertical direction are used to projectthe polygon of the building roof on the ground. Accordingly, a distortedimage produced due to the position where the photograph was taken andthe height of the building can be transformed into a shape on the groundlevel.

[0119] Subsequently, description will be made of transmission of mapinformation utilizing a map generation device according to theembodiment of the present invention. As shown in FIG. 19, a GIS center1903 receives an order (map cost) from a user 1904, and causes the mapgeneration device of the present invention to create a map having adetailed building polygon shapes by using an image received from animage provider 1902. At this time, information is added to associate themap creation source image with the created map. More specifically, theposition of a building etc. within the map creation source image, whichis appointed in the position appointment step according to theembodiment of the present invention, is associated with the buildingetc. within the created map by adding the same identification number toboth or the like. In addition, roof data of each building (name,address, height, etc. of the building) can be stored while beingassociated with the complete map data.

[0120] The map associated with the image is provided to the user 1904.

What is claimed is:
 1. A map generation device, comprising: an imageappointment unit that receives appointment of at least one position in abuilding existing within an aerial photograph; a polygon extraction unitthat extracts a building region based on a result of discriminating acolor around the appointed position, and extracts a polygon line of thebuilding region; and a vector generation unit that generates a vector ofthe polygon line of the building region.
 2. The map generation deviceaccording to claim 1, further comprising a roof texture analysis unitthat analyzes colors around the appointed position to determine samplecolors for matching, a discrimination threshold, and a region searchingrange, wherein the polygon extraction unit extracts building regionpixels based on a result of discriminating a similarity between a colorof a roof of a building in the region searching range and the samplecolors for matching, and extracts a line around the extracted buildingregion pixels as the polygon line.
 3. The map generation deviceaccording to claim 2, wherein the roof texture analysis unit extracts aplurality of pixels from a predetermined region including the appointedposition, and determines the sample colors for matching, thediscrimination threshold, and the region searching range based on aresult of statistically analyzing colors of the plurality of pixels. 4.The map generation device according to claim 3, wherein the roof textureanalysis unit expands the region of the discrimination threshold andreduces the region searching range when a variance is large in thecolors of the plurality of pixels extracted from the predeterminedregion including the appointed position.
 5. The map generation deviceaccording to claim 1, wherein the polygon extraction unit extractspixels largely different in color from adjacent pixels as edge pixels,determines boundary lines based on the edge pixels, expands theextracted building region to the boundary lines approximate to thebuilding region to correct the building region.
 6. The map generationdevice according to claim 1, wherein the polygon extraction unit rotatesthe building region so as to set the polygon line of the building regionin a predetermined axis direction, and smoothes the polygon line.
 7. Themap generation device according to claim 1, further comprising a polygoncorrection unit that, in a case where the polygon line extracted by thepolygon extraction unit corresponds to a predetermined linking pattern,corrects the polygon line to one of a straight line and lines crossingeach other at a predetermined angle.
 8. The map generation deviceaccording to claim 1, further comprising a structural analysis andintegration unit that, in a case where a line of a building roofcorresponds to a predetermined integration pattern, integrates thebuilding region so as to include the line.
 9. The map generation deviceaccording to claim 1, wherein the structural analysis and integrationunit integrates the building region appointed by a plurality of inputtedpositions.
 10. The map generation device according to claim 1, furthercomprising a ground projection unit that, in a case where the aerialphotograph shows a building obliquely, corrects distortion due to aheight of the building, and projects a building polygon shape on aground.
 11. A map delivery method, which is used to deliver a map byassociating the map created by the map generation device according toany one of claims 1 with the aerial photograph.
 12. A computer programproduct for generating a map, said computer program product comprising:receiving appointment of at least one position in a building existingwithin an aerial photograph; extracting a building region based on aresult of discriminating a color around the appointed position, and apolygon line of the building region; and generating a vector of thepolygon line of the building region.
 13. The computer program productaccording to claim 12, further comprising: analyzing colors around theappointed position to determine sample colors for matching, adiscrimination threshold, and a region searching range; extractingbuilding region pixels based on a result of discriminating a similaritybetween a color of a roof of a building in the region searching rangeand the sample colors for matching, and extracting a line around theextracted building region pixels as the polygon line.
 14. The computerprogram product according to claim 12, further, comprising: extractingpixels largely different in color from adjacent pixels as edge pixelsand determining boundary lines based on the edge pixels; expanding theextracted building region to the boundary lines approximate to thebuilding region and correcting the building region.
 15. The computerprogram product according to claim 12, further comprising: rotating thebuilding region so as to set the polygon line of the building region ina predetermined axis direction; and smoothing the polygon line after therotation.
 16. The computer program product according to claim 12,further comprising, in a case where the polygon line extracted by thepolygon extraction unit corresponds to a predetermined linking pattern,correcting the polygon line to one of a straight line and lines crossingeach other at a predetermined angle.
 17. The computer program productaccording to claim 12, further comprising: in a case where a line of abuilding roof corresponds to a predetermined integration pattern,integrating the building region so as to include the line; andintegrating the building region appointed by a plurality of inputtedpositions.
 18. The computer program product according to claim 12,further comprising, in a case where the aerial photograph shows abuilding obliquely, correcting distortion due to a height of thebuilding, and projecting a building polygon shape on a ground.